فهرست نشریات علمی دسترسی آزاد ایران

Human Activity Recognition (HAR) using computer vision is an ‎expanding field with diverse applications, including healthcare, ‎transportation, and human-computer interaction. While classical ‎approaches such as Support Vector Machines (SVM), Histogram ‎of Oriented Gradients (HOG), and Hidden Markov Models ‎‎(HMM) rely on manually extracted features and struggle with ‎complex motion patterns, deep learning-based models (e.g., ‎Convolutional Neural Networks (CNN), Long Short-Term ‎Memory (LSTM), Transformer-based models) have improved ‎performance but still face challenges in handling occlusions, ‎noisy environments, and computational efficiency. This paper ‎introduces Attention-HAR, a novel deep neural network model ‎designed to enhance HAR performance through three key ‎innovations: Conv3DTranspose for spatial upsampling, ‎ConvLSTM2D for capturing spatiotemporal patterns, and a ‎custom attention mechanism that prioritizes critical frames within ‎sequences. Unlike conventional attention mechanisms, our ‎approach dynamically assigns weights to key frames, reducing the ‎impact of redundant frames and enhancing interpretability and ‎computational efficiency. Experimental results on the UCF-101 ‎dataset demonstrate that Attention-HAR outperforms state-of-the-‎art models, achieving an accuracy of 97.61%, a precision of ‎‎97.95%, a recall of 97.49%, an F1-score of 97.64, and an AUC ‎of 99.9%. With only 1.26 million parameters, the model is ‎computationally efficient and well-suited for deployment on ‎lightweight platforms. These findings suggest that integrating ‎temporal-spatial feature learning with attention mechanisms can ‎significantly improve HAR in dynamic and complex ‎environments‏.‏